Identification of quantitative polymerase chain reaction reference genes suitable for normalising gene expression in the brain of normal and dystrophic mice and dogs

Background: In addition to progressive, debilitating muscle degeneration, ~50% of patients with Duchenne muscular dystrophy (DMD) have associated cognitive and behavioural disorders secondary to deficiency of dystrophin protein in the brain. The brain expresses a variety of dystrophin isoforms (Dp427, Dp140 and Dp71) whose functions remain to be fully elucidated. Detailed comparative analysis of gene expression in healthy and dystrophin-deficient brain is fundamental to understanding the functions of each isoform, and the consequences of their deficiency, with animal models representing a key tool in this endeavour. Reverse transcription quantitative real-time PCR (RT-qPCR) is a widely used method to study gene expression. However, accurate quantitative assessment requires normalisation of expression data using validated reference genes. The aim of this study was to identify a panel of suitable reference genes that can be used to normalise gene expression in the brain of healthy and dystrophic dogs and mice. Methods: Using the DE50-MD dog and mdx mouse models of DMD we performed RT-qPCR from fresh frozen brain tissue and employed the geNorm, BestKeeper and Normfinder algorithms to determine the stability of expression of a panel of candidate reference genes across healthy and dystrophic animals, and across different brain regions. Results: We show that SDHA, UBC and YWHAZ are suitable reference genes for normalising gene expression in healthy and dystrophic canine brain, and GAPDH, RPL13A and CYC1 in healthy and dystrophic murine brain. Notably, there was no overlap in the highest performing reference genes between the two species. Conclusions: Our findings suggest that gene expression normalisation is possible across six regions of the canine brain, and three regions of the murine brain. Our results should facilitate future work to study gene expression in the brains of normal and dystrophic dogs and mice and thus decipher the transcriptional consequences of dystrophin deficiency in the brain.


Introduction
Duchenne muscular dystrophy (DMD) is the most common lethal genetic disorder diagnosed in childhood, with a worldwide incidence of 1:3500-5000 male births (Mah et al., 2014).This muscle degenerative condition is caused by an absence or severe deficiency of the sarcolemma-associated protein dystrophin, leading to fragile muscle fibres that sustain injury under physiological use.Affected boys have progressive muscle weakness and typically require a wheelchair before their teens.The disease also affects the heart and respiratory muscles, leading to cardiac and respiratory failure usually by the mid-late twenties (Rall & Grimm, 2012).
Dystrophin is also expressed in the brain.The gene has multiple isoforms named according to their size, arising from internal promoters: muscle expresses only the largest isoform (Dp427m), but expression within the brain is more diverse, including additional full-length dystrophins (Dp427c and p) and the shorter isoforms, Dp140 and Dp71 (Figure 1).Mutations within the dystrophin gene may affect one or more isoforms.As a consequence of brain dystrophin deficiency, approximately 50% of DMD patients have neurodevelopmental and cognitive disorders, including difficulties with emotional and behavioural regulation (such as anxiety, disordered conduct, low mood) and neurodevelopmental or psychological problems (including intellectual impairment, attention deficit/hyperactivity disorder, autism spectrum disorder) (Banihani et al., 2015;Hendriksen & Vles, 2008;Pane et al., 2013).These disorders have a major negative impact on the ability of DMD patients to lead fulfilling and independent lives and add additional stresses to affected families and to healthcare systems.
Increasingly, efforts are being made to elucidate the precise expression patterns and functional roles of dystrophin with the brain.Such studies are often dependent on animal models, and several models of DMD are available, including mouse (Bulfield et al., 1984), rat (Larcher et al., 2014), dog (Kornegay, 2017;Walmsley et al., 2010) and pig (Klymiuk et al., 2013).Models vary in terms of their severity and progression of muscle pathology, cost, therapeutic tractability (Wells, 2018) and mutational Figure 1.Schematic depiction of the dystrophin gene and its transcripts.Seven promoters are identified within the dystrophin gene; three giving rise to full-length isoforms (Dp427c,m,p) and four internal promoters generating shorter isoforms (Dp260 (retinal isoform), Dp140, Dp116 (Schwann cell isoform) and Dp71).Alternate splicing of Dp71 results in the production of a further isoform (Dp40).Dp427 has multiple functional domains, subsets of which are retained by the shorter isoforms.The additional actin-binding motif of Dp427 and Dp260 isoforms is indicated with lighter shading of spectrin repeats 11-17.The site of the mdx mouse mutation in exon 23 is depicted, and of the DE50-MD dog mutation in exon 50.

Amendments from Version 1
We would like to thank the reviewers for their useful comments and questions, which we have endeavoured to incorporate/ address.We have made several edits to the text to enhance clarity and expand our methodology.Additionally, we have rearranged Table 1 to ensure consistent ordering for mice and dogs, modified Figure 1 to acknowledge that alternate splicing of Dp71 results in the production of Dp40, have altered Figure 4 to ensure all Y axes have the same scale, and have corrected inconsistencies in gene naming.
Any further responses from the reviewers can be found at the end of the article locus (Im et al., 1996).While mouse models have historically been the most commonly studied, dogs have a disease phenotype that more closely mirrors that of human DMD patients in both severity and progression at the muscle level.Canine models are thus increasingly utilised in DMD translational research (Amoasii et al., 2018;Barraza-Flores et al., 2019;Le Guiner et al., 2017) but the consequences of dystrophin deficiency within the brain in such models remains less thoroughly characterised in many aspects, including gene expression.
Reverse transcription quantitative real-time PCR (RT-qPCR) is a sensitive and accessible technique to determine the extent and potential significance of changes in gene expression: it is especially suited to directed assessment of transcriptional changes, rather than more global RNAseq approaches.Its utility in brain tissue is enormous, with the potential to study changes in transcription with ageing and disease, and to identify key disease-associated genes.The accuracy of RT-qPCR is critically dependent upon effective internal normalisation as the processing of tissue to RNA and then cDNA entails various stages, each of which may vary in efficiency and so dramatically affect subsequent quantification.Furthermore, as gene expression levels can vary between and within tissues (particularly with ageing and in disease states (Bustin, 2000;Coulson et al., 2008;Suzuki et al., 2000;Warrington et al., 2000)), normalisation of data with reference genes that exhibit stable expression under the conditions studied is essential.However, evidence suggests that frequently, no single gene is suitable in all scenarios and many reference genes show marked changes in expression between tissues and between disease states (Bustin, 2002;Butterfield et al., 2010;Chapman & Waldenstrom, 2015;Dheda et al., 2004;Maruyama et al, 2001).An ideal panel of reference genes would remain valid independent of brain region studied or whether the sample in question was healthy or dystrophic.
The minimum information for publication of quantitative real-time PCR experiments (MIQE) guidelines describe the importance of reference gene selection in RT-qPCR (Bustin et al., 2009), highlighting that all studies should use at least two reference genes, each validated for the conditions studied.Previous approaches to identification of suitable reference genes de novo have entailed evaluation of expression of multiple candidate genes, in multiple representative cDNA samples, with comparative metrics utilised to identify the most stable.Commonly-used algorithms include geNorm (Vandesompele et al., 2002a) and Bestkeeper (Pfaffl et al., 2004a), both of which use pairwise, correlation-based approaches, as well as Normfinder (Andersen et al., 2004b) that assesses expression stability of individual candidates.Reliance on a single method can be prone to bias but where a candidate gene demonstrates strong performance using all three algorithms, this provides firm support for its stability and hence its suitability as a reference gene.

Ethical considerations
This work was conducted under UK Home Office Project Licences numbers P9A1D1D6E (dog) and PPL 70-7777 (mice) and was approved by the Royal Veterinary College Ethics and Welfare committee.

Tissue collection
Dogs were euthanised by pentobarbital intravenous injection and mice by cervical dislocation.All efforts were made to ameliorate any suffering of animals with euthanasia performed by a highly experienced scientist (DW or RP).The brain was collected and dissected (delay between euthanasia and brain collection: <10 mins for mice, <2 hours for dogs).For canine brains, approximately 1-2 cm 3 samples of regions of interest (olfactory bulbs, frontal cortex, temporal cortex, occipital cortex, brainstem, cerebellum) were snap frozen in liquid nitrogen.Whole mouse brains were dissected into cortex, cerebellum and brainstem and snap frozen as above.No animals were excluded from analysis.

RNA isolation and cDNA synthesis
Frozen tissues were pulverised under liquid nitrogen using a mortar and pestle and 50-100 mg of the powder was placed directly into 1ml of TRIzol reagent (Invitrogen).RNA was extracted following the manufacturer's instructions (with inclusion of an additional 1:1 chloroform extraction following phase separation, and inclusion of 10μg glycogen during precipitation to maximise RNA yield, as previously described) (Hildyard et al., 2019;Hildyard et al., 2018).RNA purity was assessed by spectrometry (Nanodrop ND1000).Dog and mouse data were analysed separately, with datasets in the following groups: • Entire dataset (all samples, all brain regions) • Healthy samples (all brain regions, non-dystrophic only) • Dystrophic samples (all brain regions, dystrophic only) • Olfactory bulb samples (dystrophic and non-dystrophic) (canine samples only) • Cortical samples (dystrophic and non-dystrophic) • Cerebellar samples (dystrophic and non-dystrophic) • Brainstem samples (dystrophic and non-dystrophic) For Normfinder analysis, datasets were assessed either as ungrouped (to determine overall expression variation as with geNorm and Bestkeeper), or grouped (to determine variation in expression over datasets between specific, user-specified groups) as shown: • Individual animal (10 dogs, 10 mice) • Dystrophic/non-dystrophic (2 groups) • Brain region (6 in dogs, 3 in mice)

Cq determinations
For each assessed gene, individual Cq values were relatively consistent (Figure 2, Extended data Tables 1, 2 (Crawford et al., 2021c)).18S showed the highest level of expression, while ACTB and MON2 showed the lowest in dog and mouse, respectively.
Canine brain geNorm analysis.The geNorm algorithm uses a pairwise approach to rank genes by their average expression stability (M), with lower scores representing higher stability.Sequential removal of the gene demonstrating the least correlation (the highest M value), followed by recalculation of M values, results in the eventual identification of a single pair of highly correlated genes, considered the 'best pair'.
Analysis of our entire dataset (Crawford et al., 2021a), or segregated by genotype (WT or DE50-MD) revealed consistency both in overall score and in ranking, suggesting that healthy and dystrophic brains do not exhibit marked transcriptional changes (Table 2, Figure 3).The commonly accepted geNorm threshold for suitable reference gene stability is M < 0.5 (bold gene names in Table 2).SDHA and UBC formed the best pair in all three comparisons, with YWHAZ and GAPDH ranking third or fourth, while ACTB and B2M were the lowest scoring genes.
geNorm analysis by brain region again ranked ACTB lowest by all analysed subsets, with YWHAZ, SDHA and UBC highly ranked.The best pair varied by brain region, as did overall stabilities, although all subsets exhibited greater overall stability (lower M) than the combined dataset, as might be expected for more homogenous tissues.Greater diversity was found between cortical regions, with frontal and occipital cortex scoring HPRT1 and (unexpectedly) B2M highly in these datasets, which also formed the best pair when all cortical samples were combined.However, between 6 and 8 of the assessed genes scored < 0.5 in the assessed cortical regions, suggesting that these regions might exhibit generally high transcriptional stability.
Calculation of the pairwise variation resulting from inclusion of additional reference genes was also undertaken using the geNorm algorithm.This provides an indication of whether the addition of further genes will improve normalisation.While increasing the number of reference genes tended to lower the overall variation (Extended data  et al., 2002b) and the suggested "best" pair of genes alone was consistently sufficient to pass this threshold.
BestKeeper analysis.This algorithm effectively determines which gene best reflects the behaviour of the dataset as a whole.
It uses a pairwise comparison of each gene to the geometric average of all assessed genes (the 'bestkeeper'), (Pfaffl et al., 2004b).The Pearson correlation coefficient (r) is used to rank genes and r = 1 indicates perfect correlation with the bestkeeper.
SDHA, UBC, YWHAZ and GAPDH consistently scored highly, while ACTB and B2M demonstrated low correlation values in the canine brain, though here the latter was markedly lower scoring than the former (Table 3, Figure 4).In contrast to the geNorm analysis, 18S represented one of the highest scoring candidates, with r > 0.85 in 7/9 of the candidate reference genes, and the trio of UBC, SDHA and 18S were similar in score.Analysis of brains by region largely accorded with this: in most comparisons, UBC, SDHA, YWHAZ, GAPDH and 18S ranked highly, while ACTB and B2M occupied the lowest ranks.HPRT1, in contrast, scored poorly overall, yet was ranked highly in olfactory bulbs and occipital cortex specifically, and indeed performed acceptably in temporal cortex and brainstem.This interesting pattern is best explained by modest but consistent region-specific variation in expression.A gene that shows consistent good correlation with the Bestkeeper across the entire sample set will score highly overall, but a gene with excellent within-region correlations that nevertheless varies between brain regions will appear more biphasic in correlation, scoring less highly overall (Extended data Figure 1 (Crawford et al., 2021c)).
Normfinder analysis.The Normfinder algorithm differs from the pairwise approaches used by geNorm and Bestkeeper by evaluating each gene individually to determine absolute expression stability within the whole dataset or within specified subgroups (Andersen et al., 2004a).As shown (Table 4 & Table 5, Figure 5) even under this alternative assessment methodology, SDHA, UBC and YWHAZ were highly ranked in the canine samples.18S performed poorly (in agreement with geNorm, but not Bestkeeper), while RPL13A joined the top-scoring candidates.Again, HPRT1 was ranked modestly for the combined dataset, while scoring highly in brain regions individually (this gene was the highest ranked in olfactory bulbs, occipital cortex and brainstem).GAPDH ranked low in the combined scores, but interestingly was the most stable gene in the  cerebellum and temporal cortex, potentially suggesting regional differences in its expression levels and respective stability.
In agreement with both previous algorithms, B2M and ACTB were assigned poor stability values.
Under grouped analysis (Table 5) a similar pattern was observed, with SDHA, UBC and YWHAZ ranked highly, and ACTB, 18S and B2M rated as low stability candidates.Grouped analysis also suggests a 'best pair'.Interestingly, RPL13A featured in the "best pair" for six of the studied groups, often alongside SDHA.
Grouping the entire dataset by brain region produced rankings comparable to ungrouped analysis, implying that any region-specific behaviours are sufficiently marked so as to be detectable without recourse to grouping.Grouping by genotype (WT/DE50-MD) or by individual animal produced rankings similar (if not identical) to ungrouped analysis, the former comparison indicating that no candidate genes within our dataset show clear disease-association (as also suggested by geNorm and Bestkeeper).
Interestingly, when dogs were grouped by genotype, the stability values for all assessed genes were consistently lower (indicating higher stability) than when grouped by individual dog or by brain region (Figure 6).This implies that genotype, with its associated deficiency of dystrophin in the brain, accounts for less variation in gene expression than does brain region or individual.
Summary and validation.SDHA, UBC and YWHAZ are the strongest overall candidates and are suitable for normalising gene expression in healthy and dystrophic canine brain tissue, regardless of the brain region studied.To attempt to validate these candidates and ascertain if low-ranking genes might show disease association, we employed a within-dataset strategy: using the geometric mean of the highest scoring genes to normalise those that consistently ranked poorly (Figure 7).Normalisation of ACTB proved inconclusive with highly variable expression across regions and genotypes.Normalisation of B2M was more informative: while expression in most brain regions was comparable, this gene shows clear and consistent elevated expression within the olfactory bulbs.In comparison, normalisation of GAPDH (a higher scoring gene) revealed stable expression across all studied regions, in agreement with its higher ranking.
Murine brain geNorm analysis.geNorm analysis revealed RPL13A, GAPDH, SDHA and CYC1 as the highest ranking genes, but with 7 to 10 genes per grouping achieving M <0.5, it appears (as with canine brains) that individual brain regions exhibit high overall transcriptional stability (Table 6, Figure 8) (Crawford et al., 2021b).Lowest ranking genes showed some variation across regions and genotypes, but most commonly consisted of 18S and HRPT1.
As for the canine brain, calculation of the pairwise variation resulting from inclusion of additional reference genes revealed that increasing the number of reference genes to 3, 4 or 5 tended to lower the overall variation (Extended data     Bestkeeper Analysis.In the murine brain, the majority of the assessed genes were high scoring, with r > 0.85.GAPDH, CYC1 and RPL13A were consistently high scoring, while 18S, B2M and ZFP91 scoring were the lowest scoring (Table 7, Figure 9).
Normfinder Analysis.Normfinder results were in agreement with that of the Bestkeeper analysis, with GAPDH, CYC1 and RPL13A consistently highest ranking (Table 8, Figure 10).18S, HPRT1 and ZFP91 were lowest ranking.
Under grouped analysis (Table 9) a similar pattern was observed, with GAPDH, CYC1 and RPL13A ranked highly, while HPRT1, 18S and MON2 were universally rated as low stability candidates.Again, higher stability values were seen when samples were grouped by genotype, compared to when grouped by individual mouse or brain region.As with the canine samples, RPL13A performed well in the Normfinder grouped analysis and featured in the "best pair" in 5 of the 7 subgroups studied.
In summary, all three algorithms support use of GAPDH, RPL13A and CYC1 as suitable for normalising gene expression in healthy and dystrophic murine brain tissue, regardless of the brain region studied.MON2, ZFP91, 18S and HPRT1 were consistently the lowest ranking candidate reference genes, suggesting they are not an appropriate choice.
As with canine brains (above), we used the geometric mean of our three top reference genes (GAPDH, RPL13A and CYC1) to normalise three low scoring genes (MON2, ZFP91, 18S) to ascertain if these latter genes show disease association.No significant difference was found between brain from normal and dystrophic mice using the respective normalisation factor, suggesting that the expression of these lower scoring genes is not significantly altered by dystrophin deficiency within the studied brain regions.However, each gene showed variation in expression between brain regions.In comparison, UBC, a higher scoring gene, showed consistent expression levels across the three studied brain regions (Extended data Figure 1 (Crawford et al., 2021c)).

Discussion
Analysis of gene expression by RT-qPCR is a key component of the investigative toolkit in deciphering the function of dystrophin in the brain.However, the inherently dynamic  behaviour of mRNA, alongside the inevitable variations in efficiency of its extraction and conversion to cDNA, necessitate the use of a panel of stably-expressed reference genes for normalising measured expression data (Hildyard & Wells, 2014;Palombella et al., 2017;Schmittgen & Zakrajsek, 2000).In this study we have identified reference genes suitable for use in brain tissue from two important animal models of Duchenne muscular dystrophy, the DE50-MD dog and the mdx mouse.SDHA, UBC and YWHAZ had stable expression in canine brain, and GAPDH, RPL13A and CYC1 in murine brain.These genes score highly by all three utilised algorithms, both within the full dataset and when assessed as subgroups, supporting a high stability.Furthermore, the identified panels of reference genes remained valid between dystrophic and normal brain samples, as well as across brain regions.
A recent study reported YWHAZ, UBC and SDHA (alongside HMBS) to be a suitable reference gene panel for use in human brain from both normal control patients and patients with Alzheimer's disease (Coulson et al., 2008).Furthermore, YWHAZ (14-3-3 protein zeta) was a suitable reference gene in the developing and injured mouse CNS (Xu et al., 2018), and UBC (ubiquitin C) in rat cerebral cortex (Ramhoj et al., 2019).SDHA (succinate dehydrogenase subunit A) is a mitochondrial enzyme component   and is abundantly expressed in the mitochondria-rich brain.It has previously scored highly as a reference gene in mouse brain (Cheung et al., 2017) and also in canine skeletal muscle (normal and dystrophic), likely reflecting the high mitochondrial content of skeletal muscle in this species.Interestingly, however, a recent study of human brain samples found SDHA to be expressed at a low level (Ct mean >35) and so it was excluded from further analysis (Rydbirk et al., 2016, emphasising the potential difference between specific experimental designs (including primer sequences), sample types and sample quality.
Different transcripts can exhibit markedly different stabilities.
Our murine samples were collected and frozen within 10 minutes of death, compared with ~120 minutes for our canine samples, which may be sufficient time to elicit post-mortem alterations in expression patterns.We found ACTB (cytoskeletal actin beta isoform) to be expressed at very low levels in canine brain: a finding in contrast to mouse brain and reported values for human brains (Rydbirk et al., 2016).In dogs, this transcript might experience rapid turnover post-mortem, accounting for its poor ranking (a conclusion supported by similar findings in canine muscle) (Hildyard et al., 2018).Furthermore, our murine samples were collected in batches of 2 and 3 animals, while canine samples were collected from each dog on a separate date (post-mortem examinations were conducted on a single individual dog per day).Such batch effect could influence sample quality.Interestingly, the grouped Normfinder analysis revealed that greater sample variability (higher stability values) arose when dogs were grouped by individual animal, compared to when grouped by genotype.This would suggest that the brain of the dystrophin-deficient dog is transcriptionally very similar to that of the WT dog with regards to the assessed housekeeping genes, and that post-mortem changes and specific sample handling/preservation between individual animals could account for greater variability in transcript detection.Multiple factors might vary between individual animals including precise interval between euthanasia and sample collection, sample handling, and ambient temperature.Furthermore, individual genetic variation is greater in the dogs due to endeavours to maintain an outbred canine colony, compared with the inbred mouse colonies used in this study.Similar factors likely apply in assessment of other large mammals, including humans.
GAPDH (Glyceraldehyde 3-phosphate dehydrogenase) is a glycolytic enzyme frequently employed as a reference gene given its relatively constitutive expression across cell and tissue types (Penna et al., 2011).We found it to exhibit stable expression in the healthy and dystrophic murine brain, but this gene also scored comparatively highly in canine samples.In agreement with our results, GAPDH has stable expression in the human brain, particularly the cerebellum, in patients with various neurodegenerative diseases as well as in normal control patients (Coulson et al., 2008;Grunblatt et al., 2004).Interestingly, GAPDH was a poor candidate for normalisation in murine muscle, showing active disease association and prominent muscle-specific expression patterns (Hildyard et al., 2019).
RPL13A codes for a protein component of the large ribosomal subunit and scored highly in canine and murine muscle (Hildyard et al., 2019;Hildyard et al., 2018).Genes directly associated with translational machinery have been found to score highly as candidate reference genes under a variety of conditions (Kaur et al., 2018;Molina et al., 2018;Nakayama et al., 2018), implying that translational components are very stable.Interestingly, both RPL13A and CYC1 (Cytochrome C1) were identified as suitable reference genes in a study of 98 brain samples from two regions (prefrontal cortex and cerebellum) from human patients with neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Multiple System Atrophy, and Progressive Supranuclear Palsy) (Rydbirk et al., 2016).CYC1 encodes a protein that forms part of the mitochondrial electron transport chain and is crucial in cellular respiration (Schagger et al., 2004).
B2M has previously been shown to be upregulated by approximately 2-fold in canine dystrophic muscle (Hildyard et al., 2018).B2M is highly expressed in immune lineages and so the persistent inflammatory state characteristic of dystrophic muscle might feasibly increase B2M gene expression.We did not identify an upregulation of B2M in dystrophic brain tissue, which is in agreement with the lack of inflammation in brains from DMD patients (Dubowitz & Crome, 1969;Jagadha & Becker, 1988;Rosman & Kakulas, 1966).None of the candidate reference genes assessed in this study showed a significant difference in expression between healthy and dystrophic samples, suggesting that disease-specific alterations in expression patterns are likely to be subtle.Further studies to evaluate a wider spectrum of candidate genes might reveal novel disease-associated genes and so provide insights into the role of dystrophin within the brain.
The highest-scoring reference genes identified did not overlap between murine and canine brain samples, as we also reported in murine and canine muscle (Hildyard et al., 2019;Hildyard et al., 2018): in these studies, SDHA was very stable in canine muscle, but showed prominent muscle and disease-specific expression in murine muscle.This supports that specific reference genes for specific tissues should be identified for each individual model organism, rather than extrapolating between them.
The samples used to prepare our dataset were selected to offer relatively comprehensive coverage of potential variability and included healthy and dystrophic samples taken from different individuals, and from multiple brain regions.However, a number of limitations must be acknowledged: samples were derived from only five healthy and five dystrophic individuals for each studied species (dog and mouse), with six brain regions studied in the canine brain, and three in the murine brain.We have not assessed alternative DMD models, such as the mdx 3cv and mdx 4cv mouse line nor the Golden Retriever Muscular Dystrophy (GRMD) model (animal models with dystrophin mutations at different sites) and our samples do not assess changes with ageing.The overall transcriptional stability revealed by our work however, and the absence of marked differences in expression between healthy and dystrophic brains, suggests that our high scoring candidates are likely to be broadly applicable, but future studies utilising alternative brain regions, animal models or ages would nevertheless be of interest.

Conclusion
In this study we have identified reference genes suitable for use in brain tissue from two important animal models of DMD.
In the DE50-MD dog, SDHA, UBC and YWHAZ were identified as suitable for normalisation, while in the mdx mouse, the top scoring genes were GAPDH, RPL13A and CYC1.These reference genes will facilitate future work aiming to identify disease-associated genes and so improve understanding of the spectrum of neurodevelopmental and cognitive deficits identified in patients with DMD.A similar panel of reference genes appropriate for use in human samples (regardless of patient age, individual genetic background and disease status) would be of considerable benefit to the field by enabling comparison of independent studies and trials.
This project contains the following underlying data: -Raw qPCR data from canine brains (samples were assessed in triplicate; individual readings and mean sample Cq are provided).
This project contains the following underlying data: -Raw qPCR data from murine brains (samples were assessed in triplicate; individual readings and mean sample Cq are provided).
This project contains the following extended data: -Supplementary Table 1: Cq values for 9 candidate reference genes for canine brain samples.
-Supplementary Table 2: Cq values for 11 candidate reference genes for murine brain samples. -Supplementary

Hernan Gonorazky
The Hospital for Sick Children, University of Toronto, Toronto, Canada This manuscript delves into the crucial aspect of selecting appropriate housekeeping genes for RT-qPCR experiments, aiming to enable the normalization of gene expression data for dystrophin gene.Dystrophinopathy results in behavioural and cognitive impairments, which restrict patients from active participation in society.It is important to understand the different isoforms of the dystrophin gene and its expression in the brain.The housekeeping genes identified in this manuscript play a pivotal role in advancing research concerning dystrophic brains.
I agree with the authors that different ages of the models required to be studied and it has been described in the paper .
I do not have anything else to add.

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?I cannot comment.A qualified statistician is required.

Are all the source data underlying the results available to ensure full reproducibility? Yes
Are the conclusions drawn adequately supported by the results?

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.

Hui Wang
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Reverse transcription quantitative real-time PCR (RT-qPCR) is used to study gene expression, but accurate assessment requires normalization of expression data using validated reference genes.
The study aimed to identify a panel of suitable reference genes for normalizing gene expression in the brain of healthy and dystrophic dogs and mice.The study found that SDHA, UBC, and YWHAZ are suitable reference genes for normalizing gene expression in the healthy and dystrophic canine brain, while GAPDH, RPL13A, and CYC1 are suitable for the healthy and dystrophic murine brain.The findings could facilitate future research to study gene expression in the brains of normal and dystrophic dogs and mice.
Overall, the methods, results and conclusions are well organized and clearly stated.However, I do have a few suggestions and questions: The authors showed that SDHA, UBC, and YWHAZ are suitable reference genes for normalizing gene expression in the healthy and dystrophic canine brain, while GAPDH, RPL13A, and CYC1 are suitable for the healthy and dystrophic murine brain.It would be interesting to see the practical usage of those genes.For example, the authors could select a few known differentially expressed genes in dystrophic brains and see how different reference genes may give different results in terms of their expression in cases and controls. 1.
The authors could explain more on why they choose to use the geometric mean of the top 3 gene to normalize three low scoring genes.Why not use more genes or select a best one? 2.
In mice and dogs, the selected best references genes is quite different.Could authors discuss more about why that is the case?And how this difference between different organisms could help future study in humans? 3.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?Yes Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Genetics, bioinformatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
establish a panel of suitable reference genes, and the methods used within this manuscript are established, well-validated approaches that allow this determination to be made a priori, without recourse to established differentially expressed genes for corroboration.As there are no canonically "differentially expressed genes" currently established in dystrophic brains (beyond dystrophin itself, and even here isoform expression patterns may vary by mutation), the essential underlying purpose of the work shown here is to facilitate the validation of any DEGs we might identify via other means (such as RNAseq).It might interest the reviewer to note that we have already used these genes to assess expression of dystrophin isoforms within healthy and DE50-MD canine brains, and here their use allowed detection of even subtle gene expression changes (implying our methods have indeed identified viable references).
The authors could explain more on why they choose to use the geometric mean of the top 3 gene to normalize three low scoring genes.Why not use more genes or select a best one? 1.
As noted in the introduction, in accordance with the MIQE guidelines, any qPCR normalisation should endeavour to use multiple reference genes: a single gene is highly vulnerable to even modest variation (biological, experimental or technical).Two is considered the bare minimum for valid interpretation, while three renders normalisation highly robust to stochastic noise (an inherent risk in gene expression studies).In most cases, little additional stability is gained by increasing the panel from three to four (and indeed geNorm measures this empirically), and addition of yet more genes increasingly runs the risk of making the normalisation _less_ stable.Accordingly, we have avoiding implying any single gene is "best", and instead have identified the top three high-scoring candidates, and used these three to generate a combined "normalisation factor (NF)" The geometric mean is the correct averaging manipulation to combine multiple reference genes that have been linearised to relative quantities (RQ) as here.Linearised qPCR data follows a lognormal distribution and thus should be averaged in a manner appropriate for such distributions (conversely, for generating means using raw Cq values, as employed in ddCt methods, a simple arithmetic mean is appropriate).
In mice and dogs, the selected best references genes is quite different.Could authors discuss more about why that is the case?And how this difference between different organisms could help future study in humans? 1.
There are multiple reasons why this might be the case: species differences are more common than not, and expecting similar genes to be appropriate across species boundaries is ill-advised.Our work highlights exactly such an example.Furthermore, size likely plays a major role, as does cognitive capacity, both of which lead to differences in respective relative volumes of given brain regions, and their specialisations.Moreover, we studied different regions in each species: while the large size of canine brains permits greater regional division, the small size of murine brains renders comparable detail prohibitive.Additionally, while murine brains can be collected rapidly after sacrifice, canine brains necessitate longer delays between death and sample collection (see methods).Finally, our panels of reference genes were not identical between species: both panels were taken from the geNorm/geNormPLUS reference gene sets (primerdesign), and were based on datamining of a large number of public microarray datasets: if a gene is present within the canine panel, that gene has exhibited high stability across multiple datasets.If a gene is not present, likely that gene has not demonstrated stability, and would therefore make for a poor candidate anyway.We endeavoured to ensure significant overlap between our lists, allowing us to potentially identify 'universal' reference genes, but our data shows this is not the case: the same gene that performs strongly in one species might perform poorly in another.With respect to humans, we would advise against drawing unwarranted conclusions: human brains are larger than canine brains, but humans are also genetically closer to mice than to dogs.We would strongly suggest that any investigators hoping to normalise gene expression in human brains would employ a methodology similar to ours, and identify suitable reference genes a priori.

Minor points: Introduction section
In the introduction section, references are lacking for the two genes that were selected based on pig data (ACTB, YWHAZ).
In the same sentence, they refer to HRPT found in murine, while HRPT1 is consistently used throughout the manuscript.Also, both SHDA and SDHA are used, which is likely a typo.Throughout the manuscript also RPL13A and RPL13a are used.In table 6, typo in ACTB8.Please double check the spelling of gene names, and strive for consistency both in text and tables/figs.
Maybe consider underlying in tables instead of italics, such that gene names can be correctly written.I also wonder whether the reference to the dog muscle samples is required?The genes specified are already mentioned in the other selections based on the human brain (SDHA) and mouse brain (HPRT1 and RPL13a).
While authors have identified YWHAZ also based on rodent data, they refrain from testing this in the murine samples.Please explain why this was left out.
Based on which data were ZFP91 and MON2 selected for the murine samples?Authors write gene names with capitals; this only applies when referring to the human genome, but not to the i.e. the murine genome (here only the first letter is a capital).Please revise accordingly.
Figure 1 + legend: There is a red line spelling check underneath Spectrin; please remove.Would be good to write Dp40 in the figure as well i.e Dp71/40.Last sentence exon 23 is written with capital E, better to replace it with an e, to match canine exon 50.

Methods
I do wonder what authors refer to with the last bit in between brackets of the following sentence: "A total of 10 mice and 10 dogs were used in this study (experimental unit: individual animal)" The authors provide the sequence of primers used for HPRT1, but in front of the sequence, the name HPSF is written.This seems like another gene?It would be appreciated if authors could provide the sequences for all primers used in a table.
Authors specifically indicate that for the Gapdh primers design spanned intronic regions (F primer in one exons and R primer in the neighboring exon).This is indeed the accurate method for primer design to prevent amplification of genomic DNA.I do however wonder whether this was true for all other primers?If so, please be more clear about this instead of specifying this for only one primer pair.
It would be nice if the order of genes provided in table 1 and Figure 2 would match.

Results
Figure 4: It would be nice if the scaling of the axis is similar between the three graphs, as they now deviate in the number of subdivisions, being only more detailed for the DMD dogs.While in table 2 and 6, >1 genes are just combined in the table box, for table 4 and 9 the word 'and' is used.Try to be consistent.
In the legend of figure 7, the authors write: "Normalisation of B2M revealed elevated expression within the olfactory bulbs."In the figure WT levels are somewhat higher than those of DMD dogs; is this significant?Differences for this region (being elevated in DMD dogs) were found for Actb, although most likely not reaching significance due to high variability.Please rephrase, or add significance indication in figure if indeed found to be sig.elevated.

Discussion
"A recent study reported YWHAZ, UBC and SDHA (alongside HMBS) to be a suitable reference ….. and is written in italics.
Authors describe many factors that could influence the high variability seen between individual dogs.I however miss the aspect of age.Although it has not been studied to date in dogs, it is known that brain pathology progresses with age in mice (Bagdatlioglu et al. NMD 2020).Given that the age range was between 6-19 months in dogs, I wonder whether authors looked into whether the covariate age does affect results and whether variation was higher in DMD dogs compared to WT.

Supplementary files
I would encourage authors to double check labeling of their excel files and data, as there are inconsistencies: File entitled mouse brain GAPDH and S18….. has in sheet 1 labeling SDHA and UBC, while in sheet 2 this is GADPH and S18.This is just an example; please check all, since it happens with many, if not all.I would also be good to add WT and MDX + brain regions in sheet two next to the Cq values, so that one does not have to constantly switch between sheet 1 and 2 to understand the data and its labeling.
The link provided under the caption data availability; extended data referring to the figures is the same as for the excel files of the murine qPCR data.Only the link provided in the references section (Crawford et al., 2021c)  Major point: Authors have selected three brain regions to study in mice (cortex, cerebellum and brain stem), and six brain regions for the dog (olfactory bulbs, frontal cortex, temporal cortex, occipital cortex, brainstem, cerebellum).The reasoning for selecting these particular regions is lacking in the introduction section and should be added.Even though the regions that were selected are of importance, I do wonder why authors refrained from including the hippocampus and/or amygdala.Dystrophin expression is high in these particular regions (Doorenweerd et al. 2019, Sci Rep), and many of the behavioral deficits observed in patients/mice, are to the lack of dystrophin in these particular regions.I would strongly advise authors to add at least one of these highly dystrophin-abundant regions to this study.
All canine samples used in this study were taken from previously collected tissue archives (collected as part of a larger disease-characterisation study).While greater subdivision of brain regions would be advantageous, the brain regions described in the manuscript were available at the time of initiating this study, and hence were utilised in preference to the breeding and use of animals specifically for this study.This information has now been included in the introduction.Given our work suggests that a common panel of reference genes can be used to compare brain regions as different as cortex, brainstem and cerebellum, the latter of which exhibits high expression of multiple dystrophin isoforms, we do not anticipate inclusion of additional brain regions would substantially alter our findings (and concomitantly, the genes suggested are likely to be valid references for other canine brain regions).We fully acknowledge that the hippocampus and amygdala would be highly interesting and valuable regions to study and we thank the reviewer for suggesting this: we will endeavour to collect these additional brain regions in future separately funded studies.

Minor points: Introduction section
In the introduction section, references are lacking for the two genes that were selected based on pig data (ACTB, YWHAZ).We have been unable to identify the original reference for this and so have deleted this statement from the manuscript.
In the same sentence, they refer to HRPT found in murine, while HRPT1 is consistently used throughout the manuscript.Also, both SHDA and SDHA are used, which is likely a typo.Throughout the manuscript also RPL13A and RPL13a are used.In table 6, typo in ACTB8.
Please double check the spelling of gene names, and strive for consistency both in text and tables/figs.Thank you-these have now been corrected.
Maybe consider underlying in tables instead of italics, such that gene names can be correctly written.This has been modified as suggested.
I also wonder whether the reference to the dog muscle samples is required?The genes specified are already mentioned in the other selections based on the human brain (SDHA) and mouse brain (HPRT1 and RPL13a).
Our intention here is to highlight that some genes (HPRT1 and RPL13a in particular) often appear to be strong references in multiple tissue types, and even across species.While we would not suggest these genes are "universal" in any sense, we would nevertheless encourage other authors to include such genes in candidate panels when establishing appropriate references for new scenarios.
While authors have identified YWHAZ also based on rodent data, they refrain from testing this in the murine samples.Please explain why this was left out.
This was an arbitrary decision based on 11 genes being deemed sufficient, as well as availability of previously used and validated primers.
Based on which data were ZFP91 and MON2 selected for the murine samples?This information has now been added into the manuscript.
Authors write gene names with capitals; this only applies when referring to the human genome, but not to the i.e. the murine genome (here only the first letter is a capital).Please revise accordingly.
We assume the reviewer is referring to Sundberg and Schofield, and/or the specification requested by certain journals.We note that this nomenclature scheme is primarily intended to alleviate ambiguity specifically in scenarios comparing humans and rodents (for example, a mouse carrying both a human transgene and its own endogenous mouse ortholog).We are not making such comparisons, nor is any ambiguity consequently likely to result from our chosen scheme (which we have moreover employed routinely in other manuscripts, again under scenarios where human/mouse comparisons are not applicable).We further note that no similar established nomenclature guidelines appear to exist for canine genes, making the widespread adoption of this nomenclature scheme somewhat contentious: would the reviewer prefer canine genes to follow murine convention or human/NHP convention?For the sake of brevity, simplicity, and consistency with established published work, we would prefer to retain the nomenclature scheme as it presently appears.Last sentence exon 23 is written with capital E, better to replace it with an e, to match canine exon 50.This has been corrected.

Methods
I do wonder what authors refer to with the last bit in between brackets of the following sentence: "A total of 10 mice and 10 dogs were used in this study (experimental unit: individual animal)" In our experience, some confusion often surrounds the precise definition of an experimental unit, and we (in accordance with ARRIVE suggestions) included this statement to render any such ambiguity moot (i.e.regardless of number of brain regions from an individual, this remains N=1).
We now appear to have inadvertently introduced confusion by specifying this and so are happy to remove this line.
The authors provide the sequence of primers used for HPRT1, but in front of the sequence, the name HPSF is written.This seems like another gene?This is the name for the primers used by the authors in the cited reference, which we have preserved for consistency with that work.We understand HPSF to mean "HPrt1 pSeudogene Free", as pseudogene amplification is a known hazard of some reference genes.We have now additionally included HRPT1 to avoid any confusion.
It would be appreciated if authors could provide the sequences for all primers used in a table.
The bulk of the primers used in this study are proprietary (taken from the primerdesign genorm set), and consequently we do not have these sequences.In accordance with the revised MIQE guidelines, anchor nucleotides and context lengths are regarded as acceptable substitutes for sequence data: we have provided references where such information can be found.
Authors specifically indicate that for the Gapdh primers design spanned intronic regions (F primer in one exons and R primer in the neighboring exon).This is indeed the accurate method for primer design to prevent amplification of genomic DNA.I do however wonder whether this was true for all other primers?If so, please be more clear about this instead of specifying this for only one primer pair.This has been clarified as suggested.The GAPDH primers were (as noted in the manuscript) designed specifically for this work, while the other primers are either from previously published work (HPRT1) or from the primerdesign genorm/genormPLUS sets.In most cases commercial primers from the genorm set do indeed span introns, however some are non-intron spanning.We have historically validated our methodology, and these primers, to confirm that any trace genomic DNA amplification will not meaningfully contribute to our measured values (references addressing this are cited within the manuscript).
It would be nice if the order of genes provided in table 1 and Figure 2 would match This has been modified as suggested.

Results
Figure 4: It would be nice if the scaling of the axis is similar between the three graphs, as they now deviate in the number of subdivisions, being only more detailed for the DMD dogs.This has been modified as suggested.
While in table 2 and 6, >1 genes are just combined in the table box, for table 4 and 9 the word 'and' is used.Try to be consistent."and" has been removed from tables 4 and 9.
In the legend of figure 7, the authors write: "Normalisation of B2M revealed elevated expression within the olfactory bulbs."In the figure WT levels are somewhat higher than those of DMD dogs; is this significant?Differences for this region (being elevated in DMD dogs) were found for Actb, although most likely not reaching significance due to high variability.Please rephrase, or add significance indication in figure if indeed found to be sig.elevated.
The figure legend referred to the fact that B2M expression appeared to be specifically enriched in the olfactory bulb as compared to other brain regions (regardless of genotype) -we have now clarified this in the manuscript.

Discussion
"A recent study reported YWHAZ, UBC and SDHA (alongside HMBS) to be a suitable reference ….. and is written in italics.# This has been corrected.
Authors describe many factors that could influence the high variability seen between individual dogs.I however miss the aspect of age.Although it has not been studied to date in dogs, it is known that brain pathology progresses with age in mice (Bagdatlioglu et al. NMD 2020).Given that the age range was between 6-19 months in dogs, I wonder whether authors looked into whether the covariate age does affect results and whether variation was higher in DMD dogs compared to WT.
Given our small sample size and our use of dogs with very similar ages (only one dog was 6 months old and the remaining were 12-19 months) we did not investigate if age affected the data.In our recently published study evaluating MRI findings in the DE50-MD canine brain, we did not see progression of gray matter atrophy over the study period of 14-33 months, but it would certainly be interesting to further investigate if age alters the brain pathology using techniques such as cognitive testing and histopathological analysis.
Competing Interests: No competing interests were disclosed.
Author Response 27 Apr 2023

Abbe Abbe
We would like to thank the reviewer for their detailed review of our manuscript and their useful comments, suggestions and questions.
Regarding the supplementary files: File entitled mouse brain GAPDH and S18….. has in sheet 1 labeling SDHA and UBC, while in sheet 2 this is GADPH and S18.This is just an example; please check all, since it happens with many, if not all.I would also be good to add WT and MDX + brain regions in sheet two next to the Cq values, so that one does not have to constantly switch between sheet 1 and 2 to understand the data and its labeling.
These have been checked.
The link provided under the caption data availability; extended data referring to the figures is the same as for the excel files of the murine qPCR data.Only the link provided in the references section (Crawford et al., 2021c) is correct.Please check all links

Figure 2 .
Figure 2. Raw Cq values for each of the assessed genes in canine brain (A) and murine brain (B).N=10 per gene per species.

Figure 3 .
Figure 3. geNorm analysis for all canine dataset groups.The average expression stability M is shown for the full dataset or specified subgroups (ranking left to right: least stable to most stable).Dashed line: M = 0.5 (reported threshold of stability for strong candidates).

Figure 4 .
Figure 4. BestKeeper analysis of canine data.Correlation coefficients for the assessed genes are shown for the full dataset, or DE50-MD or WT dogs alone, ranked from least to most stable (left to right).

Figure 6 .
Figure 6.Normfinder analysis performed by grouping samples by genotype, by dog or by brain region.The stability value was lower (indicating higher stability) when grouped by genotype, compared to when grouped by individual dog.

Figure 5 .
Figure 5. Normfinder analysis of canine samples (ungrouped).Stability values (left to right: least stable to most stable) for the reference gene candidates are shown for the entire dataset, DE50-MD and WT samples (as indicated).

Figure 7 .
Figure 7. ACTB and B2M expression normalised to the geometric mean of SDHA, UBC and YWHAZ.Normalisation of ACTB proved inconclusive with no disease association and variable expression across the studied brain regions.Normalisation of B2M revealed > 2 fold higher expression within the olfactory bulbs in both DE50-MD and WT dogs, but no disease association.Normalisation of GAPDH showed consistent expression across all studied brain regions and both genotypes.

Figure 8 .
Figure 8. geNorm analysis for all murine dataset combinations.geNorm output ranked by average expression stability M (least stable to most stable are shown from left to right) for the full dataset, or specific subgroups (as indicated).Dashed line: M = 0.5 (reported threshold of stability for strong candidates).

Figure 9 .
Figure 9. BestKeeper analysis for murine data.Correlation coefficients for the assessed gene candidates are shown for the full dataset, or mdx or WT mice alone (as indicated), ranked from least to most stable (left to right).

Figure 10 .
Figure 10.Normfinder analysis of murine samples (ungrouped).Stability values for the assessed gene candidates are shown for the full dataset, mdx mice and WT mice (as indicated) (left to right: least to most stable).

Figure 1 +
Figure 1 + legend:There is a red line spelling check underneath Spectrin; please remove.Thank you, this has been corrected.

Table 1 . Candidate reference genes assessed in murine and canine normal and dystrophic brain samples.
trials) were kept with their mother in a large pen, to enable nursing with access to a bed under a heat lamp (~28°C).From 4 weeks of age, puppies also received puppy feed (Burns, ad lib) until weaning at 12 weeks.Dogs over the age of 12 weeks received 2 feeds daily and ad lib water.All animals follow a comprehensive socialisation programme and twice daily welfare assessments.Brain samples were obtained post-mortem from 5 male mdx mice and 5 healthy strain and age-matched male C57BL/10 WT mice (median age 7 months, range 6-8), bred under UK Home Office Project Licence and approved by the Royal Veterinary College Ethics and Welfare committee.Mice were housed in open top cages in a minimal disease unit at an average 21°C in a 12 hours light/ 12 hours dark light cycle with food and water provided ad lib.

Table 2 . geNorm output for the full canine dataset or specified subgroups.
Genes are scored from highest ranking pair (top) to least stable gene (bottom).Bold: score < 0.5 (reported threshold for suitability); underlined: score > 0.75 (considered poor reference genes).

Table 3 . Bestkeeper output for the full canine dataset or specified subgroups
. Genes are ranked from top to bottom by Pearson correlation (r) with the BestKeeper.Bold: r > = 0.85; underlineds: r < 0.6.

Table 4 . Normfinder output for the full ungrouped canine dataset or specified subgroups.
Genes are ranked (top to bottom) from highest to lowest scoring.Bold: stability <0.25; underlined: stability > 0.4.

Table 5 . Normfinder output for the full canine dataset or specified subgroups
. Genes are ranked from the highest to lowest scoring.Grouped analysis also indicates the best pair of genes for normalisation.(Bold: stability <0.25; underlined: stability > 0.4).

Table 4 (
Crawford et al., 2021c)), however the use of the suggested "best" pair of genes was consistently sufficient to exceed the acceptable threshold of 0.2(Vandesompele et al.,  2002b),

Table 6 . geNorm output for the full murine dataset or specified subgroups
. Genes are ranked from the highest scoring pair (top) to the least stable gene (bottom).Bold: score < 0.5 (threshold for suitability); underlined: score > 0.75 (poor candidate reference genes).

Table 7 . Bestkeeper output for the full murine dataset or specified subgroups.
Genes are ranked from top to bottom by Pearson correlation (r) with the BestKeeper.Bold: r > = 0.85; underlined: r < 0.6.

Table 8 . Normfinder output for the full ungrouped murine dataset or specified subgroups.
Genes are ranked (from highest to lowest scoring (top to bottom).Bold: stability <0.25; underlined: stability > 0.4.

Table 9 . Normfinder output for the full murine dataset or specified subgroups.
Genes are ranked from highest scoring (lowest stability value) to lowest scoring.Grouped analysis also suggests the best pair of genes for normalisation.(Bold: stability <0.25).

confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Version 1
This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests:
is correct.Please check all links.The way in which words are interrupted due to lack of space should be addressed in the tables.Sup table3+4; tables read better if values that match the 0.2 criteria are written in bold.No competing interests were disclosed.